Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F22%3A00555130" target="_blank" >RIV/86652079:_____/22:00555130 - isvavai.cz</a>

Result on the web

<a href="https://click.endnote.com/viewer?doi=10.1016%2Fj.rse.2021.112831&token=WzI5NjkzMTIsIjEwLjEwMTYvai5yc2UuMjAyMS4xMTI4MzEiXQ.2ErvxKTgz45sAhAIL8ihIstDfmY" target="_blank" >https://click.endnote.com/viewer?doi=10.1016%2Fj.rse.2021.112831&token=WzI5NjkzMTIsIjEwLjEwMTYvai5yc2UuMjAyMS4xMTI4MzEiXQ.2ErvxKTgz45sAhAIL8ihIstDfmY</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rse.2021.112831" target="_blank" >10.1016/j.rse.2021.112831</a>

Result language

angličtina

Original language name

Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany

Original language description

Monitoring agricultural systems becomes increasingly important in the context of global challenges like climate change, biodiversity loss, population growth, and the rising demand for agricultural products. High-resolution, national-scale maps of agricultural land are needed to develop strategies for future sustainable agriculture. However, the characterization of agricultural land cover over large areas and for multiple years remains challenging due to the locally diverse and temporally variable characteristics of cultivated land. We here propose a workflow for generating national agricultural land cover maps on a yearly basis that accounts for varying environmental conditions. We tested the approach by mapping 24 agricultural land cover classes in Germany for the three years 2017, 2018, and 2019, in which the meteorological conditions strongly differed. We used a random forest classifier and dense time series data from Sentinel-2 and Landsat 8 in combination with monthly Sentinel-1 composites and environmental data and evaluated the relative importance of optical, radar, and environmental data. Our results show high overall accuracy and plausible class accuracies for the most dominant crop types across different years despite the strong inter-annual meteorological variability and the presence of drought and nondrought years. The maps show high spatial consistency and good delineation of field parcels. Combining optical, SAR, and environmental data increased overall accuracies by 6% to 10% compared to single sensor approaches, in which optical data outperformed SAR. Overall accuracy ranged between 78% and 80%, and the mapped areas aligned well with agricultural statistics at the regional and national level. Based on the multi-year dataset we mapped major crop sequences of cereals and leaf crops. Most crop sequences were dominated by winter cereals followed by summer cereals. Monocultures of summer cereals were mainly revealed in the Northwest of Germany. We showcased that high spatial and thematic detail in combination with annual mapping will stimulate research on crop cycles and studies to assess the impact of environmental policies on management decisions. Our results demonstrate the capabilities of integrated optical time series and SAR data in combination with variables describing local and seasonal environmental conditions for annual large-area crop type mapping.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

40101 - Agriculture

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Remote Sensing of Environment

ISSN

0034-4257

e-ISSN

1879-0704

Volume of the periodical

269

Issue of the periodical within the volume

FEB

Country of publishing house

US - UNITED STATES

Number of pages

19

Pages from-to

112831

UT code for WoS article

000759682100002

EID of the result in the Scopus database

2-s2.0-85120936631